Poudre River Streamflow Analysis (2013–2023)

Load and Prepare Streamflow Data

library(dataRetrieval)

# Example: Cache la Poudre River at Mouth (USGS site 06752260)
poudre_flow <- readNWISdv(siteNumber = "06752260",    # Download data from USGS for site 06752260
                          parameterCd = "00060",      # Parameter code 00060 = discharge in cfs)
                          startDate = "2013-01-01",   # Set the start date
                          endDate = "2023-12-31") |>  # Set the end date
  renameNWISColumns() |>                              # Rename columns to standard names (e.g., "Flow", "Date")
  mutate(Date = yearmonth(Date)) |>                   # Convert daily Date values into a year-month format (e.g., "2023 Jan")
  group_by(Date) |>                                   # Group the data by the new monthly Date
  summarise(Flow = mean(Flow))                       # Calculate the average daily flow for each month

GET:https://waterservices.usgs.gov/nwis/dv/?site=06752260&format=waterml%2C1.1&ParameterCd=00060&StatCd=00003&startDT=2013-01-01&endDT=2023-12-31

1. Convert to tsibble

# Convert to tsibble
poudre_ts <- poudre_flow |>
  as_tsibble(index = Date)

poudre_ts

# A tsibble: 132 x 2 [1M]
       Date    Flow
      <mth>   <dbl>
 1 2013 Jan   18.1 
 2 2013 Feb   18.0 
 3 2013 Mar    8.21
 4 2013 Apr    5.94
 5 2013 May  333.  
 6 2013 Jun  300.  
 7 2013 Jul   75.6 
 8 2013 Aug   48.8 
 9 2013 Sep 1085.  
10 2013 Oct  146.  
# ℹ 122 more rows

2. Plot the Time Series

# Create a static ggplot
p <- ggplot(poudre_ts, aes(x = Date, y = Flow)) +
  geom_line(color = "royalblue") +
  labs(title = "Monthly Mean Streamflow: Cache la Poudre River (2013–2023)",
       x = "Date", y = "Flow (cfs)") +
  theme_minimal()

# Interactive version with plotly
ggplotly(p)

3. Subseries Plot (Seasonality)

gg_subseries(poudre_ts, Flow)

Interpretation

In the subseries plot, “seasons” are defined as calendar months (Jan–Dec). Each panel shows the variation of stream flow in that specific month across all years (2013–2023).

You can clearly observe a recurring peak in May–June, likely due to snowmelt runoff. This reflects a strong seasonal cycle. The subseries show how much stream flow changes within each month over the years, useful for identifying predictable annual patterns.

4. STL Decomposition

# STL decomposition
decomp <- poudre_ts |>
  model(STL(Flow ~ season(window = "periodic")))

# Plot components
components(decomp) |>
  autoplot()

Interpretation

The trend component shows long-term variations like multi-year increases or declines in streamflow. The seasonal component shows consistent annual peaks (May–June) and troughs (fall–winter). The remainder captures anomalies such as unexpected high flows from rain events or droughts not explained by the trend or seasonality.